What is an LLM? Understanding and using large language models

Large Language Models (LLM) are revolutionizing how we communicate and work with technology. These intelligent systems understand human language at an unprecedented level and open up completely new possibilities for companies of all sizes.

But what exactly is behind the term LLM? How do these language models work in detail and what specific advantages do they offer in everyday work? In this article, you will not only get an overview of the basics of Large Language Models, but also a deeper insight into how they work technically, from tokenization and parameters to the training process. We show practical examples of their use in companies, highlight opportunities and challenges and take a look at future developments.

The basics: What is an LLM actually?

[DEFINITION][Large Language Models][An LLM is an AI system that has been trained on massive amounts of text to understand and generate human language].

Technically speaking, this is a specialized form of machine learning: based on deep learning methods, the models recognize patterns in language and use them to generate new texts. They are based on artificial neural networks and use the so-called transformer architecture with billions of parameters.

The basic principle is surprisingly simple: the model learns through autocompletion and calculates which word or word sequence is statistically most likely to appear next. Through this process, it develops a deep understanding of language patterns and context.

Well-known examples of LLMs

The best-known large language models are

• ChatGPT by OpenAI: One of the first widely used LLMs for the public

• Claude from Anthropic: Focus on helpful and safe AI interaction

• Gemini by Google: Multimodal model with text and image capabilities

• Llama models from Meta: well-known open source alternatives for developers

• Mistral: European development with a focus on efficiency

These models differ in their architecture, computing power and specific capabilities. While some are specialized in text processing, others can also understand images or generate code.

Deep Dive: The technical functionality of LLMs

To really understand the capabilities of large language models, it's worth taking a look under the hood. The technical basis is the Transformer architecture, which was developed by Google researchers in 2017 and revolutionized the way AI systems process language.

The Transformer architecture in detail

An LLM is based on neural networks with several layers, each of which performs specific tasks. At its heart is the so-called attention mechanism, which enables the model to view different parts of a text simultaneously and recognize connections.

Imagine you read the sentence: "The dog barked loudly because it was afraid." A person intuitively understands that "he" refers to "the dog". The attention mechanism works in a similar way: it weights each word in the context of all other words and thus recognizes these connections.

Tokens and parameters: The building blocks of understanding

LLMs do not process text as whole words, but break it down into so-called tokens in a step known as tokenization. A token can be a word, a word fragment or even a single character. "Automation", for example, could be split into the tokens "car", "matisier" and "ung".

The "intelligence" is in the parameters. These are the adjusting screws of the neural network that are adjusted during training. Large models such as GPT-4 are estimated to have well over a trillion such parameters.

The training process: from data to intelligence

The training of an LLM takes place in several phases. First, the model is fed with huge amounts of text. This often involves several terabytes of data from books, articles, websites and other sources. It learns through self-monitoring: it tries to predict the next word in a sentence and adjusts its parameters based on the errors.

Modern LLMs are additionally retrained. They learn to respond to instructions and questions instead of just continuing text. This is often followed by "reinforcement learning from human feedback" (RLHF), in which human assessments help to refine the answers.

The computing power required is enormous: training large models takes weeks to months on thousands of specialized graphics processors and costs millions of dollars. This is why only a few companies such as OpenAI, Google or Anthropic concentrate on developing the largest basic models.

Custom GPTs and fine-tuning: customization to your needs

Today, companies no longer have to start from scratch if they want to use a language model for their purposes. Two approaches are available to tailor LLMs to individual requirements:

• Retrieval Augmented Generation (RAG)The model is linked to an external knowledge base so that it can access current and verified data during use. In this way, internal company information can also be integrated. Custom GPTs are an approach provided by OpenAI in the RAG area and can be understood as a subcategory. They make it possible to provide rules, examples and additional information without having to intervene in the model architecture yourself. This is ideal for quick prototypes or specialized wizards.

• Fine-tuning: Here, an existing model is specifically retrained with your own data. As a result, it permanently internalizes technical language, company processes or stylistic specifications. Fine-tuning requires more effort and technical expertise, but delivers particularly precise results if a model is to be used in a specific context in the long term.

Both methods make it possible to turn a general LLM into a tool that is precisely tailored to your company's requirements.

How LLMs can transform your business

Large language models offer concrete solutions for everyday challenges in companies. They automate time-consuming tasks and significantly improve the quality of work processes. Below we show you examples of how companies are successfully using this technology.

Intelligent customer support around the clock

Chatbots with LLM technology understand customer queries in natural language and provide appropriate answers. They can:

• Automatically answer frequently asked questions, taking into account the context of the query

• Explaining complex issues clearly and providing personalized assistance

• Analyze moods from customer feedback and react accordingly

• Relieve support teams by solving standard cases independently

• The result: shorter waiting times for customers and more time for your team to deal with complex issues.

Rethinking knowledge management

Many companies struggle with scattered information in different systems. LLMs act as intelligent knowledge assistants and, if configured and connected accordingly, search databases, emails and documents for relevant content.

You can ask in natural language: "Which contracts expire next year?" or "Who are our most important customers in Munich?" The system provides precise answers based on your internal data.

In addition, LLMs automatically create summaries of long reports or minutes. This gives you the most important information at a glance without having to spend hours working through documents.

Accelerate creative processes

The potential of LLMs extends far beyond creative workflows. They act as a flexible tool that can provide support in almost every area of a company, from software development and data analysis to the automation of administrative processes. The following examples show just how broad the application possibilities really are.

Content creation and marketing

Marketing teams use LLMs to efficiently create a wide variety of content:

• Product descriptions: Automatic generation of consistent and appealing texts

• Social media posts: Quickly create posts in the right tone of voice

• Blog article: First drafts as a basis for further editing

• Technical documentation: Structured preparation of complex issues    

The special thing about this is that the models can be adapted to your specific writing style and technical jargon. This creates authentic content that suits your company.

Optimize software development

LLMs offer particularly great advantages for developers . Code assistants such as GitHub Copilot help:

• Code generation: Creating functions from natural language descriptions

• Troubleshooting: Debugging help and detection of programming errors

• Documentation: Automatic commenting of code sections

• Learning: Explanation of new programming concepts and best practices

Studies show that developers work much more productively with AI support.

Simplify data analysis and reporting

Large Language Models analyze large amounts of unstructured data and transform them into understandable insights. They can:

• Go through thousands of customer comments or social media posts and identify the most common topics

• Summarize market reports and identify the most important trends

• Automatically create regular reports from various data sources

• Natural Language Query (NLQ), i.e. querying data using simple everyday language without technical expertise, enables analysts to retrieve results 

These capabilities significantly accelerate data-based decisions and make insights accessible to more employees .

Automate business processes intelligently

LLMs can take on repetitive tasks and act with astonishing flexibility. Typical applications include

• e-mail and order management: Instead of manually processing every incoming e-mail , LLM automatically extracts relevant information such as customer data, product requests and delivery addresses. This structured data flows directly into your order systems.

• Automated quotation generation: The system generates individual quotation drafts based on customer inquiries and historical data. Processes that used to take days can now be completed in just a few hours.

• Compliance and contract review: LLMs search contracts for specific clauses, check documents against guidelines or carry out automatic compliance checks. This reduces manual errors and speeds up checking processes considerably.

LLMs and AI agents

The use of LLMs in AI agents is developing rapidly and is already being used in many business applications. These systems combine language understanding with action competence and can not only support processes, but also control them independently to some extent. This means that practical scenarios are already emerging today in which AI agents are taking on a growing role in the automation of complex business processes. You can find more background information and specific application examples in our article on AI agents.

Challenges and important aspects

Despite all the enthusiasm for the possibilities of LLMs, you should also keep an eye on important challenges:

Hallucinations and reliability

LLMs can sometimes generate plausible-sounding but factually incorrect information. These so-called hallucinations arise because the model recognizes patterns but cannot always distinguish truth from fiction.

Solution approaches:

• Retrieval augmented generation, i.e. the combination of LLMs with external, verified data sources that can be accessed as required

• Human review of critical content

• Clear labeling of automatically generated content

Data protection and security:

The use of LLMs raises important questions about data protection. Especially with sensitive company data, you should ensure that:

• Your data will not be used for training public models

• On-premise or private cloud solutions are used for critical applications

• Clear guidelines exist for the handling of confidential information

Biases and ethical aspects:

Since LLMs are trained on large data sets, they can reflect social biases. It is important to understand this problem and establish appropriate countermeasures.

Practical steps for getting started

If you want to use LLMs in your company, a structured approach is recommended:

1. Identify use cases: Think about which repetitive or time-consuming tasks could be automated
2. Start a pilot project: start with a smaller, clearly defined area
3. Ensure data quality: High-quality training data is crucial for good results
4. Team training: employees need to learn how to use AI tools
5. Measure success: Define clear metrics for productivity increases

Developments and future of large language models

The development of LLMs is progressing rapidly. Important trends for the coming years:

Multimodal skills

Future models will not only understand text, but will also be able to process images, audio and videos. This will open up completely new use cases in corporate communications.

Domain-specific customization

Specialized models for specific industries and application areas will provide more accurate results. Medical, legal or technical LLMs better understand specialized language and context.

Improved efficiency

New algorithms and architectures reduce the computing effort so that smaller companies can also use high-performance LLMs.

Standardization through Model Context Protocol (MCP)

Another important step in the further development of LLMs is the Model Context Protocol (MCP) proposed by Anthropic. This open standard should make it possible to connect language models easily and securely with external data sources and tools.

For companies, this means more than just technical simplification: the integration effort is reduced, the introduction of new AI applications is faster and cheaper, and switching between different providers remains flexible. At the same time, confidential data can be integrated in a controlled manner without having to disclose it directly in the model training. MCP thus ensures that LLMs do not work in isolation, but are actively embedded in existing business processes. This is a prerequisite for using them productively and securely in the company.

In our article on MCP, you will find a detailed explanation of how the standard works and the specific opportunities it opens up for your company.

Utilize the potential of LLMs for your company

Large language models offer enormous potential for companies of all sizes. They can automate routine tasks, support creative processes and generate new insights from existing data. With the right strategy and appropriate security precautions, you will be able to successfully harness the benefits of this revolutionary technology for your company. The future belongs to organizations that intelligently integrate AI into their processes. LLMs are a powerful building block that will help you work more efficiently and achieve better results.

If you want to learn how to use LLMs in your company, integrate data and develop your own prototypes, our training course on the use of LLMs in companies is the ideal place to start.

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